---

---
title: "512 Final Report Yizhu Zhang"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    storyboard: true
    social: menu
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(ggplot2)
library(plotly)
```


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### Overview of the Quantified Self Movement

Anly 512 The Quantified Self Project
The Quantified self is a concept that using the private information to evaluate different aspects of life. The so called "everyday science" requires us to collect information of health, finance, habits or other perspectives in our daily life. Also, to study ourselves from a data-analysis way, we need to collect the data continuously. Before we collect the data, we also need to have one or more questions about ourselves and then find the observe the reactions in a relevant, convenient and trustworthy way. Some device like apple watch could help us to collect some basic data automatically which is a good way for the quantified self-measurement. Another important part for the quantified self concept is the well explanation and analysis. After we get all the information we need, using the analyzing method, we could have a certain pattern or draw some conclusions on the observations we have. Also, by analyzing the data we have, we could make any adjustment upon our behaviors to have a healthier and more efficient life.We use different plots and graphs to make the visualization of the data I collected and find out a better way to live.

Summary of 5 Quantified questions:

1. How many steps I walked one day and how do the steps in one day change in a monthly based duration?

2. How does the stand time vary in a month?

3. Is there a pattern for exercise hour in a month?

4. How is my spending habit? What category counts most in a monthly spending total?

5. How is my screen using habit? Is it the inefficient way of using the phone



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### Question 1 How many steps I walked one day and how do the steps in one day change in a monthly based duration?

```{r}
step<-read.csv("C:/Users/Monica/Desktop/HU/500/lab/R studio folder/512step.csv",stringsAsFactors =FALSE)
step$Step<-as.numeric(step$Step)
ggplot(step,aes(step$Date,step$Step,group=1))+
  geom_line(color="darkorchid4")+
  xlab("Date")+ylab("Daily Step")+theme(axis.text.x=element_text(angle=60, hjust=1),axis.ticks.x=element_blank(),plot.background = element_blank()) +  
  ggtitle("Monica March daily step")+theme_bw(base_size=12)

```


***
As we could tell from the plots. The daily steps varies from the begining to the end of Month largely. Since I do the work out time to time during th weeks. The steps I have here have a large difference from each other. All the daily steps are less than 8000 steps which means I have a low amount of walking or runing excercise in March. Also, in the middle of month. I went out hiking with my friends, the steps shows I have a sharp rise of daily steps in the middle of month which match the actually condition. 


### Question 2 How does the stand time vary in a month?

```{r}
stand<-read.csv("C:/Users/Monica/Desktop/HU/500/lab/R studio folder/512standhour.csv",stringsAsFactors =FALSE)
ggplot(stand,aes(stand$date,stand$hour))+
  geom_point(color="darkorchid4")+
  stat_smooth(method = "lm",color = "red")+
  xlab("Date")+ylab("Hour")+theme(axis.text.x = element_blank(),axis.ticks.x=element_blank(),plot.background = element_blank()) + 
  ggtitle("Monica Stand Time in March")+theme_bw(base_size=12)
```


***
In this chart, we could tell that the stand time fluctuateS during the week. It has a trend to going down and then rise to a certain point. Also, in some days, there are a few extreme values. It could be some events or activities happening outdoors and it will have a larger number in the standing hour compared to normal routine. The good thing is the general trend of standing time is increasing.To be healthier. I need to stand more during the day and do more exercises. 


### Question 3 Is there a pattern for exercise hour in a month?

```{r}
exercise<-read.csv("C:/Users/Monica/Desktop/HU/500/lab/R studio folder/512exercisehour.csv",stringsAsFactors =FALSE)
ggplot(exercise[exercise$hour=='hour',], aes(x=exercise$date, y=exercise$hour)) +
  geom_point(aes(col=exercise$hour, size=exercise$hour)) +
  labs(title = "My Exercise Time", x = "Date", y = "Time")
```

***
Looking at the chart, we could see the exercise condition clearly. Using the apple data in March, the time of exercise could be presented by gg plot. The lighter the point is, the longer exercise it is. The big data could be visualized well by the color and the small data reply on the shape of the point. We could see that at the first 10 days in March. I worked 3 days for more than 1.5 hour every day. And then there are about 15 days that I barely have some exercise and some even at about none. But at the last 10 days, I did the exercise between 0.5 to 1.5 hour frequently which may lead to a conclusion that the reasonable time for exercise would encourage people to have it continuously. Also, the long-time exercise could make people tired of working out for a period.


### Question 4 How is my spending habit? What category counts most in a monthly spending total?

```{r}
money<-read.csv("C:/Users/Monica/Desktop/HU/500/lab/R studio folder/512money.csv",stringsAsFactors =FALSE)
pie(money$amount, labels = money$category, main="Pie Chart of Monica's March Expense")
```


***
This pie chart represents the consumption habit of mine. We could see the most significant part of the spending is Home and Utility and Cash. The rent always makes a big part in people’s expense. Also, the Health and insurance also take a relatively important part in the March expense. Shopping is approximately equal to the groceries spending. What is more, the groceries store money is more than the restaurant one which means I love cooking myself more than going out for diners and it is a good life style by having healthier food. 



### Question5 How is my screen using habit? Is it the inefficient way of using the phone

```{r}
screenuse<-read.csv("C:/Users/Monica/Desktop/HU/500/lab/R studio folder/512screenuse.csv",stringsAsFactors =FALSE)
ggplot(screenuse) +
  aes(screenuse$classification, screenuse$Duration) +
  geom_bar(stat = "identity", color = 'Green') +
  labs(title = 'Monica Screen Use', x = 'type', y = 'Time of use')
```



***
The last bar plot shows the screen using condition of mine in a week. The social media is more than the total of all other purposes. I spent about 25 hour a week using the social media to chat and look through the pictures that others post which means I enjoy connecting with people and I like to communicate with friends and families. Also, I used about 10 hours on the google map which means I drive a lot and usually I do not remember the route. Also, I use 5 hours on phone and video. I enjoy taking the photos a lot and it is a good habit since it will take the stress off. However, I need to reduce the time using the social media since too much screen use is not good for our health. By analyzing the whole condition, I think the best way to take control of it is reducing the social media use and go out meet friends which will replace the need using the social media. Also, it would decrease the chances for depression by doing that.